Digital speech processing typically can serve several purposes in computers. In some systems, speech signals are merely stored and transmitted. Other systems employ processing that enhances speech signals to improve the quality and intelligibility. Further, speech processing is often utilized to generate or synthesize waveforms to resemble speech, to provide verification of a speaker's identity, and/or to translate speech inputs into written outputs.
In some speech processing systems, speech coding is performed to reduce the amount of data required for signal representation, often with analysis by synthesis adaptive predictive coders, including various versions of vector or code-excited coders. In the predictive systems, models of the vocal cord shape, i.e., the spectral envelope, and the periodic vibrations of the vocal cord, i.e., the spectral fine structure of speech signals, are typically utilized and efficiently performed through slowly, time-varying linear prediction filters.
These models typically utilize parameters to replicate as closely as possible the original speech signal. There tends to be numerous parameters involved in such modeling. Compression schemes are often employed to reduce the number of parameters requiring transmission in the processing system. One such technique is known as vector quantization.
Generally, vector quantization schemes, whether in speech processing or other large data modeling systems, such as image processing systems, employ a codebook or vocabulary of codevectors, and an index to the codebook. An optimal codevector in the codebook relative to an input vector is usually determined through intensive computations. An index to the optimal codevector is then transmitted. Thus, vector quantization effectively reduces the amount of information transmitted by transmitting only an indexed reference to the codevector, rather than the entire codevector. Unfortunately, the intensive computations typically involved in the determination of the optimal codevector are time-consuming and thus expensive. Accordingly, a need exists for a more efficient search strategy in vector quantization.